{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = 'Models/2022-04-29_14-55-52' # 加载需要的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55, 30, 90, 90)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import tools\n",
    "\n",
    "use_random_iter = False\n",
    "\n",
    "data = np.load('Data/test_dataset.npy') # 加载要绘制的数据\n",
    "print(data.shape)\n",
    "data_iter = tools.SeqDataLoader(data, batch_size=1, num_steps=5, use_random_iter=use_random_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 5, 30, 90, 90])\n",
      "torch.Size([1, 90, 90])\n"
     ]
    }
   ],
   "source": [
    "X, Y = next(iter(data_iter))\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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           "x": 0.05
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     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.imshow(np.array(Y[0]),zmax=140, color_continuous_scale=\"gray_r\", width=600, height=600, title=\"真实 PM10 浓度分布\")\n",
    "x = [11, 29, 47, 65, 83]\n",
    "y = [14, 32, 50, 68, 86]\n",
    "xticks=[\"115°E -\", \"120°E -\", \"125°E -\", \"130°E -\", \"135°E -\"]\n",
    "yticks=[\"50°N -\", \"45°N -\", \"40°N -\", \"35°N -\", \"30°N -\"]  \n",
    "fig.update_yaxes(tickvals=y, ticktext=yticks)\n",
    "fig.update_xaxes(tickvals=x, ticktext=xticks, tickangle=-90)\n",
    "# fig.write_image(\"Result/True.png\", scale=2)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nets import ConvGRU as net\n",
    "\n",
    "device = tools.try_gpu()\n",
    "\n",
    "RNN = net(input_dim=30, hidden_dim=64, kernel_size=5, dec_kernel_size=5,\n",
    "            num_layers=3, batch_first=True, bias=True).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load successfully.\n"
     ]
    }
   ],
   "source": [
    "tools.load(RNN, PATH, device)   # 加载模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tools.evaluate(RNN, data_iter, device)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 90, 90])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "pred, _ = RNN(X.to(device))\n",
    "pred.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
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           "gridcolor": "white",
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           "ticks": "",
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        "title": {
         "text": "预测 PM10 浓度分布"
        },
        "width": 600,
        "xaxis": {
         "anchor": "y",
         "constrain": "domain",
         "domain": [
          0,
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         "scaleanchor": "y",
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          "120°E -",
          "125°E -",
          "130°E -",
          "135°E -"
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          11,
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         ]
        },
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         "anchor": "x",
         "autorange": "reversed",
         "constrain": "domain",
         "domain": [
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          "45°N -",
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       }
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     },
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   ],
   "source": [
    "fig = px.imshow(pred.cpu().detach().numpy()[0],zmax=140, color_continuous_scale=\"gray_r\", width=600, height=600, title=\"预测 PM10 浓度分布\")\n",
    "x = [11, 29, 47, 65, 83]\n",
    "y = [14, 32, 50, 68, 86]\n",
    "xticks=[\"115°E -\", \"120°E -\", \"125°E -\", \"130°E -\", \"135°E -\"]\n",
    "yticks=[\"50°N -\", \"45°N -\", \"40°N -\", \"35°N -\", \"30°N -\"]  \n",
    "fig.update_yaxes(tickvals=y, ticktext=yticks)\n",
    "fig.update_xaxes(tickvals=x, ticktext=xticks, tickangle=-90)\n",
    "# fig.write_image(\"Result/Pred.png\", scale=2)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotRes():\n",
    "    ImageIter = iter(data_iter)\n",
    "    count = 0\n",
    "    for X, Y in ImageIter:\n",
    "        fig = px.imshow(np.array(Y[0]),zmax=140, color_continuous_scale=\"gray_r\", width=600, height=600, title=\"真实 PM10 浓度分布\")\n",
    "        x = [11, 29, 47, 65, 83]\n",
    "        y = [14, 32, 50, 68, 86]\n",
    "        xticks=[\"115°E -\", \"120°E -\", \"125°E -\", \"130°E -\", \"135°E -\"]\n",
    "        yticks=[\"50°N -\", \"45°N -\", \"40°N -\", \"35°N -\", \"30°N -\"]  \n",
    "        fig.update_yaxes(tickvals=y, ticktext=yticks)\n",
    "        fig.update_xaxes(tickvals=x, ticktext=xticks, tickangle=-90)\n",
    "        fig.write_image(\"Result/True\" + str(count) + \".png\", scale=2)\n",
    "\n",
    "        pred, _ = RNN(X.to(device))\n",
    "        fig = px.imshow(pred.cpu().detach().numpy()[0],zmax=140, color_continuous_scale=\"gray_r\", width=600, height=600, title=\"预测 PM10 浓度分布\")\n",
    "        x = [11, 29, 47, 65, 83]\n",
    "        y = [14, 32, 50, 68, 86]\n",
    "        xticks=[\"115°E -\", \"120°E -\", \"125°E -\", \"130°E -\", \"135°E -\"]\n",
    "        yticks=[\"50°N -\", \"45°N -\", \"40°N -\", \"35°N -\", \"30°N -\"]  \n",
    "        fig.update_yaxes(tickvals=y, ticktext=yticks)\n",
    "        fig.update_xaxes(tickvals=x, ticktext=xticks, tickangle=-90)\n",
    "        fig.write_image(\"Result/Pred\" + str(count) + \".png\", scale=2)\n",
    "\n",
    "        count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PlotRes()"
   ]
  }
 ],
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